" I think; therefore I am. " — René Descartes.

I research AI. My research interests lie broadly in reinforcement learning, representation learning and continual learning. Over the years, I have developed significant expertise in sequential decision making algorithms, specifically transformers and RNNs.

I completed my MSc in Computing Science at the University of Alberta and was co-supervised by Adam White and Marlos Machado; affliated with RLAI Lab and Alberta Machine Intelligence Institute (Amii). In my MSc thesis, I proposed a recurrent alternative to the transformer’s self-attention mechanism, which offers context-independent inference cost and parallelization over an input sequence. The proposed approach called the Recurrent Linear Transformer was shown to outperform state-of-the-art transformers and recurrent neural networks in partially observable reinforcement learning problems, both in terms of computational efficiency and performance. ( Thesis URL, Arxiv preprint).

I also have several years of industry experience in AI. I’m currently a senior machine learning engineer at an early-stage computer vision startup, where I apply transformers for image segmentation in architectural diagrams. During my MSc, I interned at Huawei Research Edmonton, applying reinforcement learning to neural network operator fusion. Previously, I worked with IBM Cloud as an ML Engineer (for around 2 years) and collaborated with IBM Research on various research projects in natural language processing and multi-modal learning. I also helped deploy several machine learning algorithms at scale in IBM and Kone.

Contact: spramanik [at] ualberta [dot] ca, email [at] subho [dot] in